Regime switches and commonalities of the cryptocurrencies asset class

Figà‐Talamanca, Gianna; Focardi, Sergio M.; Patacca, Marco

doi:10.1016/j.najef.2021.101425

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…The features that we use for representing market activity and public attention are new. Our findings are consistent with those of Figà-Talamanca et al, (2021), who also identify three common market regimes. Comparing our decoded state sequences with theirs we find that they are similar.…”

Section: Introductionsupporting

confidence: 91%

“…Cont, 2001 andLindström et al, 2015, Chapter 1). Figà-Talamanca et al, (2021) adopt a multivariate approach to demonstrate the presence of common market regimes among cryptocurrencies. They analyze first differences of Bitcoin, Ethereum, Litecoin, and Monero prices, and conclude that a multivariate generalized white noise Markov switching model with three states best fits the data.…”

Section: Introductionmentioning

confidence: 99%

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What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

2023

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Section: Introductionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

2023

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“…Understanding the impact of attention or sentiment on stock returns and volatility can help in the construction of profitable investment strategies 16 . Specific investment plans have also been proposed in the cryptocurrencies markets using regime switching models 17 and common dynamic factors 18 . Finally, a large amount of research is devoted to analyzing the effect of sentiment on the dynamics of stock prices and market indexes 19‐26 .…”

Section: Introductionmentioning

confidence: 99%

Sentiment‐driven mean reversion in the 4/2 stochastic volatility model with jumps

Cretarola

Figà‐Talamanca

Patacca

2023

Appl Stoch Models Bus & Ind

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SummaryWith the availability of social networks, specialized forums, and online news, sentiment analysis has become a common and useful technique for the analysis of economic and financial scenarios. Several data‐providers have also started computing proprietary sentiment indexes on financial assets to be delivered together with market price and trading volume. We develop a modified version of the mean‐reverting 4/2 stochastic volatility model introduced in Escobar‐Anel & Gong (2020) to describe the dynamics of commodities. In our specification, jumps are allowed in the asset price dynamics, and the drift coefficient may also switch between regimes related to a sentiment indicator. In this framework, we discuss the distributional characteristics of asset returns, provide a numerical procedure for model estimation, and give some preliminary results on the pricing of European‐style derivatives. Finally, the model is fitted to the market data for Gold and Crude Oil.

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“…With the appearance of an entirely new digital asset class, namely a crypto-currency market, many different studies have been carried out to analyze this market focusing on various statistical characteristics [5][6][7], the relationship between crypto-currencies and other assets [8][9][10], regime switches [11][12][13], technological aspects [14,15], etc. Among them, only few recent papers published can be found that specifically explore the trading and formation of arbitrage in a crypto-currency market.…”

Section: Introductionmentioning

confidence: 99%

Key Roles of Crypto-Exchanges in Generating Arbitrage Opportunities

Kabašinskas

Šutienė

2021

Entropy

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The evolving crypto-currency market is seen as dynamic, segmented, and inefficient, coupled with a lack of regulatory oversight, which together becomes conducive to observing the arbitrage. In this context, a crypto-network is designed using bid/ask data among 20 crypto-exchanges over a 2-year period. The graph theory technique is employed to describe the network and, more importantly, to determine the key roles of crypto-exchanges in generating arbitrage opportunities by estimating relevant network centrality measures. Based on the proposed arbitrage ratio, Gatecoin, Coinfloor, and Bitsane are estimated as the best exchanges to initiate arbitrage, while EXMO and DSX are the best places to close it. Furthermore, by means of canonical correlation analysis, we revealed that higher volatility and the decreasing price of dominating crypto-currencies and CRIX index signal bring about a more likely arbitrage appearance in the market. The findings of research include pre-tax and after-tax arbitrage opportunities.

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Regime switches and commonalities of the cryptocurrencies asset class

Cited by 11 publications

References 33 publications

What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

What Drives Cryptocurrency Returns? A Sparse Statistical Jump Model Approach

Sentiment‐driven mean reversion in the 4/2 stochastic volatility model with jumps

Key Roles of Crypto-Exchanges in Generating Arbitrage Opportunities

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